Postdoc In Microbial Genomics Jobs in Sacramento, CA

The UC Davis Genome Center is recruiting a Postdoctoral Scholar to work on a collaboration between the laboratories of Richard Michelmore (Genome Center), Xin Liu (Computer Science), and Christine Diepenbrock (Plant Sciences). This project will develop one of the first crop-specific multimodal foundation models integrating more than 100 telomere-to-telomere lettuce genomes, population-scale genomic variation, transcriptomics, and extensive phenotypic datasets to predict the consequences of allelic variation, genome editing, and genotype-by-environment interactions.

The focus of this work will be on the fine-tuning, evaluation, and multi-faceted deployment of a foundation model for lettuce. An existing DNA foundation model architecture will be leveraged while also incorporating advances due to the rapid evolution of the DNA and other -omic foundation model space. The extensive existing data sets will be leveraged for training, evaluation, validation, and use cases. The project emphasizes reproducible research and open-source software development. The successful candidate will have opportunities to publish both methodological advances in AI and biological discoveries enabled by the models.

Responsibilities:

●     Fine-tune a pretrained foundation model using lettuce genome data with applications in crop improvement.

●     Implement appropriate strategies to optimize model performance and benchmark and compare models.

●     Collaborate closely with other project team members who have expertise in lettuce genomic resources, remote sensing, plant physiology, and development to 1) curate training data, including multi-omic and phenotypic data; and 2) generate hypotheses for model training.

●     Supervise undergraduate researchers with training in machine learning.

●     Publish findings in machine learning and computational biology journals.

Required qualifications:

●     Ph.D. in Computer Science, Computational Biology, or a related field

●     Experience programming in Python

●     Demonstrated experience developing, training, or adapting deep learning models

●     An interest and willingness to learn about genome biology, gene function, and regulatory circuits

●     Willingness to collaborate in multi-disciplinary teams

●     Evidence of research productivity through publications in machine learning, computational biology, genomics, or related areas

Preferred qualifications:

●     Experience applying machine learning to biological or scientific datasets

●     Experience in training or fine-tuning LLMs

●     Experience with multimodal learning involving sequence, image, and tabular data

●     Experience with model interpretability, representation learning, or variant effect prediction

●     Experience or interest in supervising undergraduate researchers

• Those interested should apply through https://recruit.ucdavis.edu/JPF07830.